On Visual Texture Preference: Can an Ecological Model Explain Why People Like Some Textures More Than Others?

Abstract

What visual textures do people like and why? Here, we test whether the ecological valence theory proposed for color preferences can also predict people’s preferences for visual texture. According to the theory, people should like visual textures associated with positive objects or entities and dislike visual textures associated with negative objects or entities. We compare the results for the ecological model with a more traditional texture-preference model based on computational features and find that the ecological model performs reasonably well considering its lower complexity, explaining 63% of the variance in the human preference data.

Keywords

Visual texture aesthetics preference ecological valence theory

Get full access to this article

View all access options for this article.

References

Amadasun

King

(1989) Textural features corresponding to textural properties. IEEE Transactions on Systems, Man and Cybernetics 19: 1264–1274.

Anderson

B. L.

Kim

(2009) Image statistics do not explain the perception of gloss and lightness. Journal of Vision 9: 10.1–17.

Ben-Shahar

(2006) Visual saliency and texture segregation without feature gradient. Proceedings of the National Academy of Sciences of the USA 103: 15704–15709.

Bergen

J. R.

(1991) Thoeries of visual texture perception. In: Regan

(ed.) Vision and visual dysfunction Vol. 10B, New York, NY: Macmillan, pp. 114–134.

Brainard, D. H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433–436.

Brodatz

(1966) Textures: A photographic album for artists and designers, New York, NY: Dover.

Chen

Sobue

Huang

(2008) Mapping functions of color image features and human KANSEI. Paper presented at IIHMSP ’08 International Conference on Intelligent Information Hiding and Multimedia Signal Processing (pp. 725–728). Harbin, China: IEEE.

Cronbach

L. J.

(1951) Coefficient alpha and the internal structure of tests. Psychometrika 16: 197–334.

Dong

Tao

(2015) Texture classification and retrieval using shearlets and linear regression. IEEE Transactions on Cybernetics 45: 358–369.

10.

Gatto

J. A.

Porter

W. A.

Selleck

(2010) Exploring visual design: The elements and principles, 4th ed. Worcester, MA: Davis Publications.

11.

Guo

Zhang

(2010) Rotation invariant texture classification using LBP variance (LBPV) with global matching. Pattern Recognition 43: 706–719.

12.

Haindl

Filip

(2013) Visual texture: Accurate material appearance measurement, representation and modeling, London, England: Springer-Verlag.

13.

Haralik

R. M.

Shanmugam

Dinstein

(1973) Texture features for image classification. IEEE Transactions on Systems, Man and Cybernetics 3: 610–621.

14.

Y. X.

Landy

M. S.

Maloney

L. T.

(2006) How direction of illumination affects visually perceived surface roughness. Journal of Vision 6: 634–648.

15.

Joshi

Datta

Fedorovskaya

Luong

Wang

J. Z.

Luo

(2011) Aesthetics and emotions in images. IEEE Signal Processing Magazine 28: 94–115.

16.

Julesz

(1962) Visual pattern discrimination. IEEE Transactions on Information Theory 8: 84–92.

17.

Julesz

(1981) Textons, the elements of texture perception, and their interactions. Nature 290: 91–97.

18.

Kawamoto

Soen

(1993) Objective evaluation of color design II. Color Research and Application 18: 260–266.

19.

Kim

Anderson

B. L.

(2010) Image statistics and the perception of surface gloss and lightness. Journal of Vision 10: 3.

20.

Kim

E. Y.

Jeong

Kim

(2006) Emotion-based textile indexing using colors, texture and patterns. In: Bebis

(eds) Advances in visual computing, Berlin, Germany: Springer, pp. 9–18.

21.

Kleiner, M., Brainard, D., & Pelli, D. (2007). What’s new in Psychtoolbox-3? Perception, 36, 14. (ECVP Abstract Supplement).

22.

Landy

M. S.

Bergen

J. R.

(1991) Texture segregation and orientation gradient. Vision Research 31: 679–691.

23.

Landy

M. S.

Graham

(2004) Visual perception of texture. In: Chalupa

L. M.

Werner

J. S.

(eds) The visual neurosciences, Cambridge, MA: MIT Press, pp. 1107–1118.

24.

Lee

Park

(2011) Fuzzy similarity-based emotional classification of color images. IEEE Transactions on Multimedia 13: 1031–1039.

25.

Liu

Lughofer

Zeng

(2015) Could linear model bridge the gap between low-level statistical features and aesthetic emotions of visual textures? Neurocomputing 168: 947–960.

26.

Lucassen

M. P.

Gevers

Gijsenij

(2011) Texture affects color emotion. Color Research and Application 36: 426–436.

27.

Malik

Perona

(1990) Preattentive texture discrimination with early vision mechanisms. Journal of the Optical Society of America A 7: 923–932.

28.

Montoyishi

Nishida

Sharan

Adelson

E. H.

(2007) Image statistics and the perception of surface qualities. Nature 447: 206–209.

29.

Palmer

S. E.

Schloss

K. B.

(2010) An ecological valence theory of human color preference. Proceedings of the National Academy of Sciences of the USA 107: 8877–8882.

30.

Palmer

S. E.

Schloss

K. B.

Sammartino

(2013) Visual aesthetics and human preference. Annual Review of Psychology 64: 77–107.

31.

Portilla

Simoncelli

(2000) A parametric texture model based on joint statistics of complex wavelent coefficients. International Journal of Computer Vision 40: 49–71.

32.

Randen

Husy

J. H.

(1999) Filtering for texture classification: A comparative study. IEEE Transactions on Pattern Analysis and Machine Intelligence 21: 291–310.

33.

Rao

A. R.

Lohse

(1993) Towards a texture naming system: Identifying relevant dimensions of texture perception. (Research Report No RC19140 (83352)). Almaden: IBM Research Division.

34.

Rao

A. R.

Lohse

G. L.

(1996) Towards a texture naming system: Identifying relevant dimensions of texture. Vision Research 36: 1649–1669.

35.

Ritter

N. L.

(2010) Understanding a widely misunderstood statistic: Cronbach's alpha. Paper presented at Southwestern Educational Research Association Conference, New Orleans, LA.

36.

Rosenholtz

(2000) Significantly different textures: A computational model of pre-attentive texture segmentation. In: David

(ed.) Computer vision – ECCV 2000, Berlin, Germany: Springer, pp. 197–211.

37.

Rosenholtz

(2015) Texture perception. In: Wagemans

(ed.) Oxford handbook of perceptual organization, Oxford, England: Oxford University Press, pp. 167–186.

38.

Schloss

K. B.

Goldberger

C. S.

Palmer

S. E.

Levitan

C. A.

(2015) What's that smell? An ecological approach to understanding preferences for familiar odors. Perception 44: 23–38.

39.

Schloss

K. B.

Hawthorne-Madell

Palmer

S. E.

(2015) Ecological influences on individual differences in color preference. Attention, Perception, and Psychophysics 77: 2803–2816.

40.

Schloss

K. B.

Palmer

S. E.

(2014) The politics of color: Preference for republican-red vs. democtratic-blue. Psychonomic Bulletin and Reivew 21: 1481–1488.

41.

Schloss

K. B.

Poggesi

R. M.

Palmer

S. E.

(2011) Effects of university affiliation and “school spirit” on color preferences: Berkeley versus Stanford. Psychonomic Bulletin and Review 18: 498–504.

42.

Sebe

Lew

M. S.

(2001) Texture features for content-based retrieval. In M. S. Lew (Eds.), Principles of visual information retrieval, London, England: Springer-Verlag, pp. 51–85.

43.

Tamura

Mori

Yamawaki

(1978) Textural features corresponding to visual perception. IEEE Transactions on Systems, Man and Cybernetics 8: 460–473.

44.

Taylor

Franklin

(2012) The relationship between color-object associations and color preference: Further investigation of the ecological valence theory. Psychonomic Bulletin and Review 19: 190–197.

45.

Thumfart

Heidl

Scharinger

Eitzinger

(2009) A quantitative evaluation of texture feature robustness and interpolation behaviour. In: Jaing

Petkov

(eds) Computer analysis of images and patters: Lecture notes in computer science Vol. 5702, Berlin, Germany: Springer, pp. 1154–1161.

46.

Thumfart

Jacobs

R. H.

Lughofer

Eitzinger

Cornelissen

F. W.

Groissboeck

Richter

(2011) Modeling human aesthetic perception of visual textures. ACM Transactions on Applied Perception 8: Article 27.

47.

Tuceryan

Jain

(1998) Texture analysis. In: Chen

C. H.

Pau

L. F.

Wang

P. S.

(eds) The handbook of pattern recognition and computer vision, 2nd ed. Hackensack, NJ: World Scientific Publishing Co, pp. 207–248.

48.

Tyler

C. W.

(2004) Theory of texture discrimination of based on higher-order perturbations in individual texture samples. Vision Research 44: 2179–2186.

49.

Eum

Lee

(2002) A study of the emotional evaluation models of color patterns based on the adaptive fuzzy system and the neural network. Color Research and Application 27: 208–216.

50.

Varma

Zisserman

(2005) A statistical approach to texture classification from single images. International Journal of Computer Vision 62: 61–81.

51.

Victor

J. D.

(1988) Models for preattentive texture discrimination: Fourier analysis and local feature processing in a unified framework. Spatial Vision 3: 263–280.

52.

Yokosawa

Yano

Schloss

K. B.

Prado-Leon

L. R.

Palmer

S. E.

(2010) Cross-culteral studies of color preferences: US, Japan, and Mexico [abstract]. Journal of Vision 10: 408.

53.

Zhang

Augilius

Honkela

Laaksonen

Gamper

Alene

(2011) Analyzing emotional semantics of abstract art using low-level image features. In J. Gama, E. Bradley, & J. Hollmén (Eds.), Proceedings of the 10th International Conference on Advances in Intelligent Data Analysis, Berlin, Germany: Springer-Verlag, pp. 413–423.